Spectral Analysis for Sparse Matrix Computation: Insights and Potential
Sparse computations are fundamental to scientific computing, graph analytics, and machine learning, yet their performance is highly sensitive to the diverse sparsity and patterns. This is because cache reuse, memory coalescing, and load balancing depend critically on the sparsity patterns. This work gives the first known exploration of the connections between sparse matrix computation and spectral analysis by treating sparse matrices as two-dimensional signals and analyzing their frequency-domain representations through Fast Fourier Transform. We show that spectral signatures uncover global structural characteristics that are not sufficiently captured by conventional spatial statistics an...
| Authors | Ruifeng Zhang, Xipeng Shen |
|---|---|
| Primary category | cs.PF |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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